Toward precision radiotherapy: a nonlinear optimization framework and an accelerated machine learning algorithm for the deconvolution of tumor-infiltrating immune cells

HIGHLIGHTS

SUMMARY

The second broad category, deconvolution-based methods, considers the gene_expression profile of the heterogeneous tumor sample as a convolution of the gene_expression levels of the different cell components; as a result, they can quantitatively estimate the fractions of cell types of interest (in this case TIICs). This consideration allows the problem to be formulated mathematically as a function of the gene_expression profiles of the cell-type admixture. Given the bulk gene_expression of a tumor sample and a known cell-type specific expression profile, solving an inverse problem can estimate the cell-type fractions in . . .

 



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